{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "428d6758",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import mindspore as md\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd # 读取文件\n",
    "from sklearn.utils import shuffle # 打乱数据\n",
    "from sklearn.preprocessing import scale # 打乱数据\n",
    "# df = pd.read_csv(\"data/boston.csv\", header=0)\n",
    "\n",
    "# print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aad65286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             CRIM         ZN       INDUS         CHAS         NOX          RM  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
      "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
      "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
      "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
      "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
      "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
      "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
      "\n",
      "              AGE         DIS         RAD         TAX     PTRATIO       LSTAT  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean    68.574901    3.795043    9.549407  408.237154   18.455534   12.653063   \n",
      "std     28.148861    2.105710    8.707259  168.537116    2.164946    7.141062   \n",
      "min      2.900000    1.129600    1.000000  187.000000   12.600000    1.730000   \n",
      "25%     45.025000    2.100175    4.000000  279.000000   17.400000    6.950000   \n",
      "50%     77.500000    3.207450    5.000000  330.000000   19.050000   11.360000   \n",
      "75%     94.075000    5.188425   24.000000  666.000000   20.200000   16.955000   \n",
      "max    100.000000   12.126500   24.000000  711.000000   22.000000   37.970000   \n",
      "\n",
      "             MEDV  \n",
      "count  506.000000  \n",
      "mean    22.532806  \n",
      "std      9.197104  \n",
      "min      5.000000  \n",
      "25%     17.025000  \n",
      "50%     21.200000  \n",
      "75%     25.000000  \n",
      "max     50.000000  \n",
      "(506, 13)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/boston.csv\", header=0)\n",
    "\n",
    "print(df.describe())\n",
    "print(df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95a3f62a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90bd2a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d50d08",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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